A Comparative Study of Machine Learning Models for Early Stage Identification of Powdery Mildew on Cherry Leaf

Agriculture is the primary source of assistance for all the key sections of our country, INDIA. Population growth and Urbanization are the two major factors that create serious challenges for this field. According to the report of the Food and Agriculture Organization of the United Nations, by the next two decades, there will be a need for IT infrastructure to handle the rapidly expanding dietary needs of the two billion human population. Precision agriculture is the smart aid to resolving these issues. it allows for more efficient use of resources. It helps farmers to meet the growing demand for food sustainably by implementing smart IT techniques for several activities like Site-specific planting and fertilization, Crop monitoring, pest management, and variable rate irrigation. Autonomous vehicles, etc. keeping this in view a comparative study on advanced machine learning. Support vector machine, K-Nearest Neighbor, Artificial Neural Network, and Decision Tree algorithms have been implemented on MATLAB TOOL for early-stage identification of powdery mildew on cherry leaf. Hereafter considering various performance metrics like Confusion metrics parameters (accuracy, recall, precision), and ROC & AUC curve, it is found that ANN provides the best results of 91% on 1,200 images of the standard open source repository.